Filtering

Filtering is often required to reduce the data of a MassComposition object to a specific subset of interest.

Both individual objects can be filtered, as can multiple objects contained within a Flowsheet object.

import pandas as pd

from elphick.mass_composition import MassComposition
from elphick.mass_composition.flowsheet import Flowsheet
from elphick.mass_composition.datasets.sample_data import sample_data

Create a MassComposition object

We get some demo data in the form of a pandas DataFrame

df_data: pd.DataFrame = sample_data()
df_data

	wet_mass	mass_dry	FE	SIO2	al2o3	LOI	group
index
0	100.0	90.0	57.0	5.2	3.0	5.0	grp_1
1	90.0	80.0	59.0	3.1	1.7	4.0	grp_1
2	110.0	90.0	61.0	2.2	0.9	3.0	grp_2

Construct a MassComposition object

obj_mc: MassComposition = MassComposition(df_data, name='demo')
obj_mc.data.to_dataframe()

	mass_wet	mass_dry	H2O	Fe	SiO2	Al2O3	LOI	group
index
0	100.0	90.0	10.000000	57.0	5.2	3.0	5.0	grp_1
1	90.0	80.0	11.111111	59.0	3.1	1.7	4.0	grp_1
2	110.0	90.0	18.181818	61.0	2.2	0.9	3.0	grp_2

Filtering Single Objects

One of the most common subsets is one that contains records above a particular grade. The method used to filter is called query, for consistency with the xarray and pandas methods that execute the same.

obj_1: MassComposition = obj_mc.query({'index': 'Fe>58'})
obj_1.data.to_dataframe()

	mass_wet	mass_dry	H2O	Fe	SiO2	Al2O3	LOI	group
index
1	90.0	80.0	11.111111	59.0	3.1	1.7	4.0	grp_1
2	110.0	90.0	18.181818	61.0	2.2	0.9	3.0	grp_2

Notice that the record with an Fe value below 58 has been removed.

Filtering Multiple Objects

Multiple objects can be loaded into a Flowsheet. We’ll make a small network to demonstrate.

obj_one, obj_two = obj_mc.split(fraction=0.6, name_1='one', name_2='two')

fs: Flowsheet = Flowsheet.from_streams([obj_mc, obj_one, obj_two], name='Network')

The weighted mean mass-composition of each object/edge/stream in the network can be reported out with the report method.

fs.report()

	mass_wet	mass_dry	H2O	Fe	SiO2	Al2O3	LOI
name
demo	300.0	260.0	13.333333	59.0	3.515385	1.873077	4.0
one	180.0	156.0	13.333333	59.0	3.515385	1.873077	4.0
two	120.0	104.0	13.333333	59.0	3.515385	1.873077	4.0

Now we’ll filter as we did before, though we must specify which object the query criteria is to be applied to.

fs.query(mc_name='demo', queries={'index': 'Fe>58'}).report()

	mass_wet	mass_dry	H2O	Fe	SiO2	Al2O3	LOI
name
demo	200.0	170.0	15.0	60.058824	2.623529	1.276471	3.470588
one	120.0	102.0	15.0	60.058824	2.623529	1.276471	3.470588
two	80.0	68.0	15.0	60.058824	2.623529	1.276471	3.470588